Adsorbates can trigger surface reconstruction on metal surfaces, a common yet highly important phenomenon in heterogeneous catalysis that has not been fully explored. Here, we develop a reliable Cu–C–O machine learning force field (MLFF) with ab initio accuracy, providing insights into the reconstruction mechanism and distribution of active sites on the Cu surface under a CO atmosphere through state-of-the-art deep potential molecular dynamics (DPMD). Combining statistical cluster analysis with microkinetic modeling, we establish a strategy to quantitatively assess the turnover frequency (TOF) of catalyst surfaces during the dynamic catalytic process. Our findings reveal that edge Cu atoms undergo rearrangement, ejection, diffusion, and aggregation under a CO atmosphere, leading to the formation of cluster active sites. These small clusters in dynamic equilibrium are identified as the origin of the high catalytic activity of Cu-based catalysts for a low-temperature water–gas shift reaction (WGSR). This work not only elucidates intrinsic activity in metal catalysis and the dynamic catalysis theory but also offers valuable insights for computational catalysis methods to identify effective catalysts for practical applications.

中文翻译：

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水-气变换反应过程中 Cu 催化剂上反应物诱导的动态活性位点


吸附物可以触发金属表面的表面重构，这是多相催化中常见但非常重要的现象，尚未得到充分探索。在这里，我们开发了一个可靠的 Cu-C-O 机器学习力场 （MLFF），具有从头到顶的精度，通过最先进的深电位分子动力学 （DPMD） 深入了解 CO 气氛下 Cu 表面活性位点的重建机制和分布。将统计聚类分析与微动力学建模相结合，我们建立了一种策略来定量评估动态催化过程中催化剂表面的周转频率 （TOF）。我们的研究结果表明，边缘 Cu 原子在 CO 气氛下经历重排、喷射、扩散和聚集，导致形成簇活性位点。这些处于动态平衡状态的小团簇被确定为铜基催化剂在低温水-气变换反应 （WGSR） 中高催化活性的来源。这项工作不仅阐明了金属催化中的内禀活性和动态催化理论，还为计算催化方法确定了实际应用的有效催化剂提供了有价值的见解。